The objective of the study was to develop and validate predictors of 30-day hospital readmission using readily available administrative data and to compare prediction models that use alternate comorbidity classifications. A retrospective cohort study was designed; the models were developed in a two-thirds random sample and validated in the remaining one-third sample. The study cohort consisted of 29,292 adults aged 65 or older who were admitted from July 2002 to June 2004 to any of seven acute care hospitals in the Dallas-Fort Worth metropolitan area affiliated with the Baylor Health Care System. Demographic variables (age, sex, race), health system variables (insurance, discharge location, medical vs surgical service), comorbidity (classified by the Elixhauser classification or the High-Risk Diagnoses in the Elderly Scale), and geographic variables (distance from patient's residence to hospital and median income) were assessed by estimating relative risk and risk difference for 30-day readmission. Population-attributable risk was calculated. Results showed that age 75 or older, male sex, African American race, medical vs surgical service, Medicare with no other insurance, discharge to a skilled nursing facility, and specific comorbidities predicted 30-day readmission. Models with demographic, health system, and either comorbidity classification covariates performed similarly, with modest discrimination (C statistic, 0.65) and acceptable calibration (Hosmer-Lemeshow χ² = 6.08; P > 0.24). Models with demographic variables, health system variables, and number of comorbid conditions also performed adequately. Discharge to long-term care (relative risk, 1.94; 95% confidence interval, 1.80- 2.09) had the highest population-attributable risk of 30-day readmission (12.86%). A 25% threshold of predicted probability of 30-day readmission identified 4.1 % of patients ≥65 years old as priority patients for improved discharge planning. We conclude that elders with a high risk of 30-day hospital readmission can be identified early in their hospital course.

No abstract

Rural and small community hospitals typically have few resources and little experience with quality improvement (QI) and, on average, demonstrate poorer quality of care than larger facilities. Formalized QI education shows promise in improving quality, but little is known about its effect in rural and small community hospitals. The authors describe a randomized controlled trial assigning 47 rural and small community Texas hospitals to such a program (n = 23) or to the control group (n = 24), following provision of a Web-based quality benchmarking and case review tool. Centers for Medicare and Medicaid Services Core Measures composite scores for congestive heart failure (CHF) and community-acquired pneumonia (CAP), using Texas Medical Foundation data collected via the QualityNet Exchange system, are compared for the groups, for 2 years postintervention. Given the estimated baseline rates for the CHF (68%) and CAP (66%) composites, the cohort enables the detection of 14% and 11% differences (alpha = .05; power = 0.8), respectively.

While the observed results suggest no incremental benefit of the quality improvement educational program following implementation of a web-based benchmarking and case-review tool in rural hospitals, given the small number of hospitals that completed the program, it is not conclusive that such programs are ineffective. Further research incorporating supporting infrastructure, such as physician champions, financial incentives and greater involvement of senior leadership, is needed to assess the value of quality improvement educational programs in rural hospitals.

In the months before death, patients with CHF were more likely to have care plans directed at disease modification and treatment, whereas dementia patients were more likely to have care plans that focused on symptom relief and anticipation of dying. Several factors may contribute to this difference.